Verification of fraudulent activities at a self-checkout terminal

ABSTRACT

A verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation utility patent application whichclaims the benefit to and priority from U.S. patent application Ser. No.14/139,731 filed on Dec. 23, 2013.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates generally to a security system and methodto prevent fraudulent activities occurring at a self-checkout terminal.More particularly, a method and system for verifying a fraudulentactivity at a self-checkout unit.

Description of Related Art

Self-checkouts are quickly becoming the established method of paying forretail goods by consumers. Their promise of shorter lines, quickersales, and increased privacy are appealing to consumers. Furthermore,not having to staff manned-checkouts and the monetary savings associatedwith decreased payroll expenses is appealing to the retailer.

U.S. Pat. No. 5,965,861 to Abby et al. teaches a self-service checkoutterminal equipped with a processing unit that determines 1) whether theitem was successfully scanned; 2) whether the scanned or un-scanned itemis placed into the grocery bag. Abby et al. teaches a system and methodfor providing security during operation of a self-service checkoutterminal by detecting movement of an item within a scanner zoneassociated with the terminal with a video system and generating ascanning-attempt control signal in response thereto. The item beingscanned over the system is monitored to determine if any item is beingbagged without being scanned. The video system detects the motion of theitem to determine whether the item has passed across the scanner.

WO 2005/038733 to Asao teaches a register system which includes ashopping cart and a register for accounting of the commodities containedin the shopping cart. The shopping cart is provided with a barcodereader for reading a commodity code and weight information from abarcode attached to a commodity; a weighing machine for weighing theweight of the commodity contained in a containing unit; and acommunication unit for receiving information indicating that an unjustaction of the customer has been detected according to the analysis ofthe image picked up by a monitoring camera provided in the shop. Asaoteaches a method that verified a fraudulent activity of the customer bymonitoring and detecting a suspicious motion of the customer using asurveillance camera positioned to monitor the store.

U.S. Pat. No. 5,747,784 to Walter et al. discloses a method andapparatus for checking out an item through a self-service checkoutstation. A scanner generates a first signal which detects a machinereadable code associated with an item. A recorder generates a visualrecording of the motion being scanned. The recorder (video camera)detects the motion of the customer scanning the item across the scannerdevice. The processing unit determines whether the productidentification code of the item is successfully captured by the scanner.The verification of the motion of the item is checked by the recordedmotion from the recorder and the scanner, in order to verify whether anitem entering a grocery bag has been successfully scanned. A weightscale also may be utilized to monitor and verify the item's placement,for example monitoring the insertion of items into and the removal ofitems from the grocery bags.

Netherland Patent No. 1004940 discloses a method and apparatus fordetecting fraudulent activities occurring by the customer at a point ofsales system. Each item is place on a slopping and transparent platewhere the moving belt carries the item over a barcode scanner. Atransparent cover is placed over the item to prevent it from beingswitched. A video camera registers any movement of the item placed onthe moving belt. The system determines whether the item is acceptableand sorts them out.

On the other hand, the self-checkouts present a number of problems. Theopportunity, ease, and potential of deniability allow some customers tosteal items by simply not scanning them or by feigning trouble scanningitems. Therefore, thefts or fraudulent activities occurring at theself-checkouts can often be several times that of manned checkouts.

Accordingly, self-checkouts often employ various anti-theft measuresutilizing a weight sensor or scale that measures the weight of theoutput at bagging area. If the increase of the weight at the baggingarea is not commensurate with the item's known weight, the system raisesa flag, and the transaction is halted. The self-checkouts are oftenerroneous and set off too many “false-positives.” In addition, theftdeterrence of self-checkouts is not comprehensive enough which allowsadditional ways that customers can steal from self-checkouts.

Therefore, what is needed is a system and verification process that canreduce theft activities and false-positive alerts of a fraudulentactivities that may occur at self-checkouts.

SUMMARY OF THE INVENTION

The subject matter of this application may involve, in some cases,interrelated products, alternative solutions to a particular problem,and/or a plurality of different uses of a single system or article.

In one aspect, a method of verifying a possible fraudulent activity at aself-checkout terminal is provided. The method is operated via acomputer processing unit. A video feed generated from a video sourcemonitoring an item during at least a portion of a transaction time at atransaction area is obtained. The computer processing unit obtains adata feed from a data source where the data feed is generated bymonitoring the item during at least a portion of the transaction time atthe transaction area. The video feed then are analyzed to identify thepossible fraudulent activity by comparing a preset data of an expectedvideo feed to at least a frame of the video feed. The data feed may beanalyzed to identify the possible fraudulent activity. An alert isissued when both the analyzed video feed and the analyzed data feedconfirm the possible fraudulent activity.

In another aspect, a method of verifying a possible fraudulent activityat a self-checkout terminal is provided. A computer processing unitreceives an unexpected weight data of an item from the self-checkoutterminal where, the weight sensor is coupled with the self-checkoutterminal to obtain the unexpected weight data of the item being placedon the weight sensor. Next, the possible fraudulent activity isidentified by comparing the unexpected weight data of the item to anexpected weight tolerance. The unexpected weight data is analyzed toverify whether the unexpected weight data corresponds with the expectedweight tolerance. The computer processing unit further identifies thepossible fraudulent activity based on a video feed, where the video feedis generated from a video source monitoring the item at a transactionarea. At least a frame of the video feed may be compared to an expectedvideo feed corresponding to the item at the transaction area.

In yet another aspect, a system for verifying a possible fraudulentactivity at a self-checkout terminal is provided. The system comprises avideo source communicatively coupled with a computer processing unitwhich may be in communication with the self-checkout terminal. The videosource may generated a video feed of an item placed at a transactionarea by monitoring the item during at least a portion of a transactiontime. The system further comprises a weight sensor communicativelycoupled with the self-checkout terminal, where a weight of the item maybe measure by the weight sensor. The measured weight may be transmittedto the computer processing unit from the self-checkout terminal. Thecomputer processing unit may identify the possible fraudulent activityby comparing at least a frame of the video feed to an expected videofeed corresponding to the item. Further, the computer processing unitmay identify the possible fraudulent activity by comparing the weight ofthe item to an expected weight tolerance. An alert may be issued by thecomputer processing unit if both comparisons confirm the possiblefraudulent activity.

In a further aspect, a method of identifying a loss incident at aself-checkout terminal is provided. A computer processing unit obtains avideo feed, wherein the video feed is generated from a video sourcemonitoring an item at a transaction area. The video feed is generatedduring at least a portion of a transaction time. The computer processingunit identifies the loss incident based on the obtained video feed by avideo analysis, and then modifies the video feed to simplify it. Themodified video feed is transmitted to a human validation terminal toperform a human validation. The human validation response is thenreceived to identify the loss incident based on the human validation. Analert is issued based on the video analysis and the human validationresponse.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides an embodiment of a method of verifying a possiblefraudulent activity at a self-checkout terminal.

FIG. 2 provides an embodiment of the method of identifying a lossincident at the self-checkout terminal.

FIG. 3 provides an embodiment of a video feed modification which isbeing sent for a human validation.

FIG. 4 provides an embodiment of a human validation process.

FIG. 5 provides an embodiment of a system for verifying a possiblefraudulent activity at a self-checkout terminal.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of presently preferred embodimentsof the invention and does not represent the only forms in which thepresent invention may be constructed and/or utilized. The descriptionsets forth the functions and the sequence of steps for constructing andoperating the invention in connection with the illustrated embodiments.

The issued U.S. Pat. No. 7,631,808 describes in detail the process ofvideo analysis and transaction data analysis. As such, the issued U.S.Pat. No. 7,631,808 is incorporated herein by reference. The examinersare invited to furnish the electronic copy of the aforementioned issuedpatent.

In its essence, U.S. Pat. No. 7,631,808 describes methods and apparatusfor detecting a transaction outcome such as suspicious activity relatedto a transaction (e.g., purchase, refund, void, etc.) of items by acustomer at a transaction terminal. The system obtains video dataassociated with a transaction area. The video data may be obtained, forexample, from an elevated camera focused on a cash register check out orother transaction area in a supermarket or other retail establishment.The system applies an automated machine video analysis algorithm that isdisclosed as part of the system to analyze at least a portion of thevideo data to obtain at least one video parameter concerning at least aportion of a transaction associated with the transaction area. As anexample, the system can analyze the video data to track (e.g. identifythe presence of) items involved in the transaction in the transactionarea. This process can automatically identify the presence of an iteminvolved in the transaction from the video data analysis. This can bedone, for example, by automatically detecting item activity in thetransaction area and/or detecting operator activity in the transactionarea. Detection of item presence can include detecting removal of anitem from a region of interest in the transaction area and/or detectingintroduction of an item into a region of interest in the transactionarea.

Using the combination of video analysis in comparison with transactiondata, the system can determine if the presence of the item identified inthe analysis of the video data has a corresponding presence in thetransaction data, and if not, identifies the suspicious activity. As anexample, for each detector, the system can compare the set of detectionevents for that detector to at least a portion of the transaction datato identify at least one apparent discrepancy in a number of itemsdetected by that detector from a number of items indicated in theportion of transaction data. Transaction data such as transaction count(e.g. scan count) or transaction item identity thus represents thepresence of an item or a number of items scanned (for an entiretransaction), while the detection event data or video count from thevideo analysis represents the presence (or number) of items that theoperator causes to move through the transaction area. Many otherembodiments are disclosed herein, details of which are provided in thedetailed description section.

Generally, the present invention concerns a method of verifying apossible fraudulent activity which may occur during a self-checkouttransaction by a customer. The present invention enhances the operationof a self-checkout operation by integrating the self-checkout operationwith additional verification measures to confirm the possible fraudulentactivity identified by a self-checkout terminal. The enhancement isfacilitated by combining one or more of the following data feeds: avideo analysis, transaction data integration, weight integration, and anoptional human validation process. The combination ensures a possiblefraudulent activity initially identified by the self-checkout terminaland verifies the legitimacy of the possible fraudulent activity. Inturn, the present invention reduces false-positive alert given to thepossible fraudulent activity where the alert is only issued based on asingle verification.

Examples of possible fraudulent activities may include, but are notlimited to, when a customer at the self-checkout terminal places an itemthat is not being transacted in a bagging area; places another item inplace of the item being purchased in a bagging area; places an itemoutside of the bagging area, such as on the floor, input area, or otherareas not specifically designated for collection of purchased item,without transacting it through the self-checkout terminal; and takesunpurchased items following the end of the transaction; and the like.

Examples of false-positive fraudulent activities may include, but arenot limited to, when non-merchandise items are placed in a bagging areasuch as purses, cell phone, umbrella, outerwear, etc.; free merchandiseitems, such as promotional item placed in a bagging area; merchandiseitems not associated with the current transaction, such as itemspurchased previously from the same store or from another store placed ina bagging area; triggering of weight discrepancies caused by partiallyconsumed items, merchandise items whose weight changes with age, orother types of correctly scanned; leaning or sitting on thebagging/output area scale causing unexpected weight increase; use ofreusable bags; items falling off of the output area, and the like.

Video source contemplated herein may include, but are not limited to, avideo camera such as, analog cameras and IP cameras, and the like; adevice that can provide a video feed of any duration, such as a DVR; aportable computing device having a camera, such as a tablet computer,laptop computer; and the like.

Data feed contemplated herein may be in the format including, but arenot limiting to, XML, JSON, CSV, binary, over any connection type:serial, Ethernet, etc. over any protocol: UDP, TCP, and the like.

In one embodiment, a possible activity of interest at a self-checkoutterminal may be verified. The method and system disclosed herein may notbe limited to identifying and verifying a possible fraudulent activity.The possible activities of interest may be activities at theself-checkout terminal that are not fraudulent activities. The possibleactivity of interest may include, but are not limited to, a delaybetween a plurality of items being passed through the self-checkoutterminal, a mechanical error occurring to the self-checkout terminal, apaper jam of a receipt printing at the self-checkout terminal, acustomer distress call, and the like, The examples of the possibleactivity of interest that are not the possible fraudulent activity mayor may not require an intervention or an alert, thus, may implement averification process without issuing the intervention or the alert.

A system for implementing the disclosed method of verifying a possiblefraudulent activity at a self-checkout terminal is provided. The systemmay comprise a computer processing unit, such as a Digital VideoAnalyzer (DVA), a video source, a weight sensor, and the self-checkoutterminal.

The computer processing unit may be operable to analyze a data feedgenerated by monitoring an item being scanned at the self-checkoutterminal. The data feed may comprise a video feed, a transaction data,and an unexpected weight data. The data feed may be obtained by thecomputer processing unit to identify the possible fraudulent activity.The identified possible fraudulent activity may be further verifiedbased on an analysis conducted over another type of the data feed. Thevideo feed may be obtained from the video source monitoring an itemplaced at a transaction area. The unexpected weight data may be a resultof analyzing a weight measure from the weight sensor. The weight sensormeasures the weight of the item being placed at a bagging area. Theunexpected weight data may be obtained when the weight of the item doesnot correspond with an expected weight tolerance.

In one embodiment, the expected weight tolerance may be a range ofacceptable weight assigned to the item. For example, an apple may beassigned the expected weight tolerance based on an average weight of anapple.

In another embodiment, the unexpected weight data may indicate that theweight of the item is outside the range of acceptable weight specifiedby the expected weight tolerance. If the weight of the item is outsidethe expected weight tolerance, the unexpected weight data may identifythe possible fraudulent activity.

In yet another embodiment, the unexpected weight data may indicate thatthe weight of the item is inside the expected weight tolerance, thusdenying the possible fraudulent activity.

In yet another embodiment, the item may not be assigned with theexpected weight tolerance. In this embodiment, the unexpected weightdata may identify the possible fraudulent activity by indicating thatthe item does not have an existing weight tolerance, therefore theweight of the item does not correspond with the expected weighttolerance.

In a further embodiment, the unexpected weight data may indicate adifference between the weight of the item measured from the weightsensor and the expected weight tolerance. For example, if the weight ofthe item is heavier and outside the expected weight tolerance, theunexpected weight data may indicate such difference. The expected weighttolerance also may be a single number, and the unexpected weight datamay indicate the difference in a similar manner.

The system for implementing the disclosed method of verifying a possiblefraudulent activity at a self-checkout terminal may further comprise ahuman validation terminal to perform a human validation. The humanvalidation terminal may analyze a portion of the video feed to verify oridentify the possible fraudulent activity.

In one embodiment, the computer processing unit may be operable tomodify the video feed where the video feed may be simplified. Themodification of the video simplifies the video feed such that the humanvalidation terminal operated by a video analyst can process the modifiedvideo feed in real-time via the human validation. A detailed process ofthe human validation is disclosed further in the disclosure.

Simplifying the video feed can be as simple as clipping it into ashort-duration snippet, in order to review only a portion of the videofeed. It can also involve image processing in order to facilitate thehuman validation. It can also involve taking snapshots from the videofeed. It can also include rotating, scaling, and performing other affinetransforms to the video feed in order to regularize the look of thevideo for consistency of review purposes. All of these are to beunderstood as simplifications of the video feed.

A plurality data feed may be generated per an item being transacted atthe self-checkout terminal. The possible fraudulent activity may firstbe triggered by one of the plurality of data feeds, then verified.Typically, the self-checkout terminal has a built-in mechanism which mayinitially flag the possible fraudulent activity. In order to ensure thatthe initially flagged possible fraudulent activity does need an alert oran intervention, more than one of the plurality of data feeds may beutilized to verify the possible fraudulent activity, confirming ordenying the possible fraudulent activity. Each of the plurality of datafeeds may be transmitted to a computer processing unit electronicallycommunicating with the self-checkout terminal. Each of the plurality ofdata feeds may be analyzed in comparison to a preset data.

The preset data refers to an expected data feed assigned to each itemsbeing sold at a store. Examples of the preset data may include, but arenot limited to, the expected weight tolerance, an expected shape of theitem, an expected number of counts of the item, and the like.

In one embodiment, the possible fraudulent activity may be verifiedutilizing a weight sensor. The weight sensor may be electronicallycoupled to a bagging area of the self-checkout terminal, providing aweight of an item placed in the bagging area. The provided weight of theitem may be compared to the expected weight tolerance of the item.

The expected weight tolerance may be assigned to each items being soldat the store, setting a tolerance level that may be accepted. When theweight of the item exceeds or falls behind the expected weight toleranceof the item, the item may be flagged as the possible fraudulentactivity. An item may be assigned more than one of the expected weighttolerance. For example, the item may come with a bonus item during a buyone get one free event.

The expected weight tolerance may be utilized to capture an item beingplaced in the bagging area where the item is not an item categorized inthe store. In such case, the expected weight tolerance may be set tozero as a default to flag the item having a weight that is not an itemcategorized in the store.

The unexpected weight data may be generated accordingly as disclosedherein. The possible fraudulent activity may be identified based on theunexpected weight data where the unexpected weight data is obtained whena weight of an item from the weight sensor does not correspond with theexpected weight tolerance.

In another embodiment, a video analysis may be utilized to verify thepossible fraudulent activity. A video feed may be generated by a videosource monitoring activities at a transaction area. The transaction areais not limited to a certain section of the self-checkout terminal, butmay include surrounding areas of the self-checkout terminal. The videoanalysis may be conducted via the computer processing unit where thevideo feed is analyzed frame by frame to identify the possiblefraudulent activity. The frame-by-frame analysis may identify an itemthat is not being sold at the store, in which case such activity may notbe the possible fraudulent activity. The video feed of the item beforeand after being placed at the bagging area may be analyzed to verify thepossible fraudulent activity. Various video analysis techniques areavailable and the method and process of the video analysis pertaining tothe present invention is being incorporated herein by reference.

In yet another embodiment, a transaction data may be correlated to thevideo analysis in order to verify the possible fraudulent activity. Suchmethod and process is also being furnished herein by aforementionedincorporation by reference.

A possible fraudulent activity occurring at a self-checkout terminal maybe verified to determine if an intervention is necessary. Theintervention may be in the form of an alert given to the self-checkoutterminal electronically, such as halting the self-checkout terminal. Thealert may be given to a self-checkout attendant where the self-checkoutattendant may proceed with the intervention.

In one embodiment, the alert may be issued if the possible fraudulentactivity is confirmed based on two of the plurality of data feed.

In another embodiment, the alert may be denied if the possiblefraudulent activity identified by one of the plurality of data feedindicated a false-positive case.

In yet another embodiment, the alert may be issued even if each of theplurality of data feed indicates a different results. In thisembodiment, the human validation may be conducted to further verify thepossible fraudulent activity. By of example, a bunch of bananas may beverified as a single banana based on the video feed, but the bunch ofbananas may be outside the expected weight tolerance depending on thenumber of bananas in the bunch. In such a case, the human validation mayfurther verify the possible fraudulent activity before issuing thealert.

The possible fraudulent activity may be verified further by a humanvalidation. The human validation may utilize the video feed produced bythe video source. The video feed may be modified where the video feed issimplified, then the modified video feed may be sent to a humanvalidation terminal from the computer processing unit, followed byverifying whether the modified video confirm the possible fraudulentactivity. The human validation may be found useful in cases where thepossible fraudulent activity is difficult and/or confusing to beverified by other machine-enabled means of verification. The humanvalidation terminal may be operated by a video analyst.

In one embodiment, the human validation may be conducted as a final stepto confirm the possible fraudulent activity being verified by theplurality of data feed.

The modification of the video feed disclosed herein may include imageanalysis and processing approaches that can be used to facilitate thehuman validation. By way of non-limiting examples, such modification mayinclude, but are not limited to, highlighting differences between one ormore images to show regions of discrepancy; cropping or masking theimagery to eliminate distracting details and to focus attention on thepertinent areas of the scene; rotating and subjecting the imagery totransformations (both affine and non-linear, e.g., rotating, stretching,shearing, mirroring, eliminating distortion, etc.) to put the imageryinto a consistent canonical view; highlighting regions in the imagewhere items are introduced, removed, manipulated, or moved; highlightingoptical flow; highlighting motion trails; highlighting of previouslyvalidated items of merchandise; overlay transaction log andself-checkout event information (e.g., weight sensor information,scanner information, skip-bagging button, customer presence detectioninformation, electronic article security tag deactivation, tag removal,etc.); highlighting specific image regions designated for specificactivity (e.g., input area, output area, etc.); etc.

The verification of the possible fraudulent activity may be conductedwith the human validation in real-time.

The human validation may be considered to make up for deficiencies innon-human enabled verification method of the possible fraudulentactivity. For example, tuning automated parameters in an automatedverification system will give the system a specific false positive rateand true positive rate. For example, if such a system is tuned to yielda true positive rate of 50%, the false positive rate may be too high. Inthis case, combining the automated approach with specific humanvalidation may yield a composite system capable of maintaining thedesired true positive yield while reducing the false positive rate tosomething more manageable.

In one embodiment, the human validation may be processed utilizing a webservice. The modified video feed may be communicated via a web browserutilizing the web service having access to an internet connection. Theanalyst conducting the human validation may utilize a web application toreview and verify the alert being sent from the computer processingunit. The web application may call into the web service via long pollingmechanisms in order to create an event driven interface forverification/validation. If no validation jobs are in the web service,the long polling mechanism eventually may time out and be immediatelyreestablished by the web application. If a job is available, the longpolling call returns with the job's details. Subsequent web calls may bemade from the web application to the web service in support of the job.In the meantime, when the DVA has job requiring human validation, itwill call into the web service with job details. This call may notimmediately return.

In one embodiment, the job may go to the web service job queue, whichwill give the job to a waiting web application client. Once the job iscompleted, the human validation result is then passed on to the waitingcomputer processing unit/DVA call.

A loss incident may be verified by the video analysis. The lossincidents contemplated herein may include, but are not limited to, whena customer neglects an item in a cart, a theft activity, and the like.The loss incident may occur when the item is not being placed at thebagging area, disabling the weight sensor from performing its function.The lost incident may undergo the human validation to verify whether theloss incident truly occurred or not.

In one embodiment, the loss incident at a self-checkout terminal may beverified by obtaining a video feed generated from the video sourcemonitoring an item at a transaction area. The video feed may be apartial video feed of the item during a transaction time. Thetransaction time indicates a period of time from the item beingintroduced to the self-checkout terminal to a payment being successfullytransacted. The video analysis of the video feed conducted by thecomputer processing unit may be verified by the human validationperformed at the human validation terminal. If the loss incident isconfirmed, an alert or an intervention may be issued.

Turning now to FIG. 1, an embodiment of a method of verifying a possiblefraudulent activity at a self-checkout terminal is described. First, theself-checkout terminal flags a possible intervention 101; anintervention or an alert may be needed if an incident at theself-checkout terminal is a fraudulent activity. The self-checkoutterminal may initially obtain a data feed generated from theself-checkout terminal by monitoring an item being transacted, andidentify the possible fraudulent activity. The self-checkout terminalrequests a validation 102 of the identified possible fraudulentactivity. A central processing unit may utilize a plurality of data feedto process 103 the incident and determined whether an intervention isneeded. The plurality of data may be analyzed to verify whether thepossible fraudulent activity is confirmed or denied. The humanvalidation may be applied optionally in order to confirm or deny thepossible fraudulent activity 108.

If the analysis of one or more of the plurality of data confirms 104that the possible fraudulent activity is indeed fraudulent, theself-checkout terminal may request the intervention 106 where theintervention may be given in the form of an alert appearing on theself-checkout unit or an alert being sent to a self-checkout attendantnotifying to intervene the transaction being processed at theself-checkout terminal.

If the analysis of one or more of the plurality of data denies 105 thepossible fraudulent activity, the incident may be ignored and nointervention would take place 107.

FIG. 2 shows an embodiment of the method of identifying a loss incidentat the self-checkout terminal. When the loss incident 201 occurs at theself-checkout terminal, the visual analysis 202 is conducted by thecentral processing unit to verify whether the loss incident is confirmedor false. The central processing units identifies the loss incident 203from the visual analysis and alerts the self-checkout unit 204, therebythe intervention is requested. The human validation 206 may be appliedoptionally in order to confirm or deny the visual analysis beingconducted for the loss incident. An alert is given to the self-checkoutunit once the loss incident is confirmed which may be followed byrequesting an intervention 205.

FIG. 3 shows an embodiment of a video feed modification which is beingsent for the human validation conducted by a video analyst. FIG. 3 showsa modified video feed of an item being scanned. First it shows asnapshot being taken at scanning time, followed by a snapshot taken atbagging time. Highlights are applied to modify the video feed bydifferencing the snapshot where the highlights indicate pertinentchanges in the bagging area. Silhouettes of the highlighted video feedthan is obtained via thresholding, followed by morphological operations,and blob based connected component analysis to simplify the video feedto show a single item being added to the bagging area.

FIG. 4 provides an embodiment of a human validation. The humanvalidation may be conducted first by receiving a human validationrequest 402. Before sending out the alert of the intervention, the humanvalidation may be initiated 401 by the DVA 400 or the central processingunit. A video feed form the video source may be modified and sent to theweb service 404. The modified video feed (images T-LOG) is then queuedat the job queue 406 by the job creator 403. Once the human validationjob is sent 407 to be processed, a video analyst uses the humanvalidation terminal 408 or a web application (WA) to conduct the humanvalidation of the modified video feed. Once the human validation job iscompleted 405 the response 409 of the human validation is sent back tothe DVA.

FIG. 5 shows an embodiment of a system for verifying a possiblefraudulent activity at a self-checkout terminal. A computer processingunit 509 communicates with a self-checkout terminal 508. Theself-checkout terminal 508 is connectively coupled to a weight sensor506 which measure a weight of a plurality of items placed in a baggingarea 505. The video camera 507, electronically communicating with thecomputer processing unit 509, may monitor activities on a transactionarea 501, 504 which may also monitor a cart 502 and a customer 503. Inthis embodiment, a video feed is generated by the video camera andtransmitted to the central processing unit. The computer processing unitmay identify the possible fraudulent activity based on the video feed. Aweight sensor may also be utilized in the present system to verify thepossible fraudulent activity. The unexpected weight data may identifythe possible fraudulent activity based on a weight of each of theplurality of items and an expected weight tolerance corresponding toeach of the plurality of items. The computer processing unit mayauthorize an alert/intervention to be given based on the unexpectedweight data.

While several variations of the present invention have been illustratedby way of example in preferred or particular embodiments, it is apparentthat further embodiments could be developed within the spirit and scopeof the present invention, or the inventive concept thereof. However, itis to be expressly understood that such modifications and adaptationsare within the spirit and scope of the present invention, and areinclusive, but not limited to the following appended claims as setforth.

What is claimed is:
 1. A method of reducing a false-positive alertissued to a possible fraudulent activity at a self-checkout terminal,the method operated via a computer processing unit, comprising the stepsof: obtaining a first identification of the possible fraudulent activityby analyzing a video feed generated from a video source wherein thevideo feed is generated by monitoring an item during at least a portionof a transaction time, the item being located at a transaction area ofthe self-checkout terminal, wherein the first identification selectivelydenies or confirms the possible fraudulent activity; obtaining a secondidentification of the possible fraudulent activity by analyzing a datafeed from a data source wherein the data feed is generated by monitoringthe item during at least a portion of the transaction time, wherein thesecond identification selectively denies or confirms the possiblefraudulent activity; determining the possible fraudulent activity as afalse-positive incident if at least one of the first and secondidentifications denies the possible fraudulent activity; and denying anintervention by ignoring the first identification and the secondidentification.
 2. The method of claim 1 further comprising the stepsof: modifying the video feed wherein the video feed is simplified;transmitting the modified video feed to a human validation terminal toperform a human validation; and receiving a human validation responsefrom the human validation terminal to confirm the possible fraudulentactivity.
 3. The method of claim 2 further comprising the step ofidentifying the possible fraudulent activity as the false-positiveincident when both the first and second identifications confirm thepossible fraudulent activity, but the human validation response does notconfirm the possible fraudulent activity.
 4. The method of claim 2wherein the step of modifying the video feed comprises at least one of:highlighting the video feed; highlighting a difference between two ormore frames of the video feed; clipping the video feed into ashort-duration snippet; and applying affine transform to the video feed.5. The method of claim 1 wherein the step of obtaining a secondidentification of the possible fraudulent activity by analyzing a datafeed from a data source comprises at least one of: analyzing the datafeed from a transaction data wherein the transaction data is generatedfrom the self-checkout terminal as the item is scanned at theself-checkout terminal; and analyzing the data feed from a weight sensorwherein the weight sensor detects a weight of the item placed on theweight sensor.
 6. The method of claim 5 wherein the step of analyzingthe data feed from a weight sensor comprises: receiving the weight ofthe item from the self-checkout terminal, the weight sensor beingcommunicatively coupled to the self-checkout terminal wherein theself-checkout terminal obtains the weight of the item being placed onthe weight sensor; and obtaining the second identification by comparingthe weight of the item to an expected weight tolerance.
 7. The method ofclaim 1 further comprising the step of issuing an alert if both thefirst and second identifications confirm the possible fraudulentactivity.
 8. A method of reducing a false-positive alert issued to apossible fraudulent activity at a self-checkout terminal, the methodoperated via a computer processing unit, comprising the steps of:obtaining a first identification of the possible fraudulent activity byanalyzing a video feed generated from a video source wherein the videofeed is generated by monitoring an item during at least a portion of atransaction time, wherein the video feed is analyzed frame-by-frame, theitem being located at a transaction area of the self-checkout terminal,wherein the first identification selectively denies or confirms thepossible fraudulent activity; obtaining a second identification of thepossible fraudulent activity by analyzing a weight of the item, a weightsensor communicatively coupled with the self-checkout terminal whereinthe self-checkout terminal obtains the weight of the item being placedon the weight sensor, and the second identification being obtained bycomparing the weight to an expected weight tolerance, wherein the secondidentification selectively denies or confirms the possible fraudulentactivity; determining the possible fraudulent activity as afalse-positive incident if at least one of the first and secondidentifications denies the possible fraudulent activity; and denying anintervention by ignoring the first identification and the secondidentification.
 9. The method of claim 8 further comprising the stepsof: modifying the video feed wherein the video feed is simplified;transmitting the modified video feed to a human validation terminal toperform a human validation; and receiving a human validation responsefrom the human validation terminal to confirm the possible fraudulentactivity.
 10. The method of claim 9 further comprising the step ofidentifying the possible fraudulent activity as the false-positiveincident when at least one of the first and second identificationsconfirms the possible fraudulent activity, but the human validationresponse does not confirm the possible fraudulent activity.
 11. Themethod of claim 9 wherein the step of modifying the video feed comprisesat least one of: highlighting the video feed; highlighting a differencebetween two or more frames of the video feed; clipping the video feedinto a short-duration snippet; and applying affine transform to thevideo feed.
 12. The method of claim 8 further comprising the step ofissuing an alert if both the first and second identifications confirmthe possible fraudulent activity.
 13. A system for reducing afalse-positive alert issued to a possible fraudulent activity at aself-checkout terminal comprising: a video source communicativelycoupled with a computer processing unit, the computer processing unit incommunication with the self-checkout terminal, the video source operableto generate a video feed of an item placed at a transaction area bymonitoring the item during at least a portion of a transaction time; adata source communicatively coupled with the self-checkout terminal, thedata source generating a data feed by monitoring the item during atleast a portion of the transaction time wherein the data feed istransmitted to the computer processing unit from the data source; andthe computer processing unit is configured to: obtain a firstidentification of the possible fraudulent activity by analyzing thevideo feed, wherein the first identification selectively denies orconfirms the possible fraudulent activity; obtain a secondidentification of the possible fraudulent activity by analyzing the datafeed, wherein the second identification selectively denies or confirmsthe possible fraudulent activity; determine the possible fraudulentactivity as a false-positive incident if at least one of the first andsecond identifications denies the possible fraudulent activity; and denyan intervention by ignoring the first identification and the secondidentification.
 14. The system of claim 13 further comprising: a humanvalidation terminal communicatively coupled with the computer processingunit wherein the human validation terminal receives a simplified videofeed generated by the computer processing unit, the human validationterminal transmitting a human validation response verifying the possiblefraudulent activity.
 15. The system of claim 14 wherein the simplifiedvideo feed comprises at least one of: highlighting the video feed;highlighting a difference between two or more frames of the video feed;clipping the video feed into a short-duration snippet; and applyingaffine transform to the video feed.
 16. The system of claim 13 whereinthe step of obtaining a second identification of the possible fraudulentactivity by analyzing a data feed from a data source comprises at leastone of: analyzing the data feed from a transaction data wherein thetransaction data is generated from the self-checkout terminal as theitem is scanned at the self-checkout terminal; and analyzing the datafeed from a weight sensor wherein the weight sensor detects a weight ofthe item placed on the weight sensor.
 17. The system of claim 16 whereinthe step of analyzing the data feed from a weight sensor comprises:receiving the weight of the item from the self-checkout terminal, theweight sensor being communicatively coupled to the self-checkoutterminal wherein the self-checkout terminal obtains the weight of theitem being placed on the weight sensor; and obtaining the secondidentification by comparing the weight of the item to an expected weighttolerance.
 18. The system of claim 13 wherein the computer processingunit is further configured to issue an alert if both the first andsecond identifications confirm the possible fraudulent activity.